Level 1 Analyses

Level 1 analyses:

  • Identify and extract cohorts of interest based on user-defined options, including exposures, outcomes, continuous enrollment requirements, incidence criteria, inclusion/exclusion criteria, relevant age groups, and demographics
  • Calculate descriptive statistics for the cohort(s) of interest 
  • Perform unadjusted and minimally adjusted analyses (i.e., stratification by Data Partner, age group, sex, and year) 

Below are the different types of cohort identification strategies for Level 1 analyses.

Level 1 Cohort Identification Strategies

Type 1: Calculate Background Rate

What this program does:

  • Identifies prevalent or incident events (exposure, outcome, condition)
  • Calculates the rate of that event in the Sentinel Distributed Database (SDD) during a user-defined query period 

Output metrics include:

  • Number of individuals with the event
  • Eligible members
  • Eligible member-days

This program calculates, reports, and stratifies rates by user-defined: 

  • Age group
  • Sex
  • Year
  • Year-month
  • Race (available upon request)
  • Ethnicity (available upon request)
  • Geographic region (available upon request)

An attrition table is also provided upon request.

Continue reading about background rate calculation on Sentinel's Git Repository. 

What this program does:

  • Identifies prevalent or incident exposures of interest
  • Determines exposed time (either requester-defined number of days after treatment initiation or based on drug dispensings’ days supplied)
  • Looks for the occurrence of a health outcome of interest (HOI) during exposed time 

Output metrics include:

  • Number of exposure episodes 
  • Number of exposed individuals
  • Number of exposure episodes with an event
  • Days at-risk
  • Eligible members
  • Eligible member-days

This program calculates, reports, and stratifies rates by user-defined: 

  • Age group
  • Sex
  • Year
  • Year-month 
  • Race (available upon request)
  • Ethnicity (available upon request)
  • Geographic region (available upon request)

Incidence rate ratios (IRRs) using two identified cohorts (i.e., exposed vs. active-comparator cohort) can be calculated. Unadjusted IRRs and adjusted IRRs (adjusted by age group, sex, year, and Data Partner using Poisson regression) are provided upon request. An attrition table is also provided upon request.

Continue reading about exposures and follow-up time on Sentinel's Git Repository.

What this program does:

  • Identifies concomitant exposure to two medical products
  • Creates concomitant treatment episodes
  • Looks for the occurrence of a health outcome of interest (HOI) during exposed time 

Concomitant exposure is overlapping exposure to two medical products. Several options are available to create concomitant treatment episodes, including adding exposure extension periods and restriction to episodes of a minimum duration. Output metrics include: 

  • Number of concomitant exposure episodes 
  • Number of exposed patients
  • Number of concomitant exposure episodes with an event
  • Days at-risk

Continue reading about on identifying episodes of concomitant use Sentinel's Git Repository. 

What this program does: 

  • Specifies a primary treatment episode
  • Defines an observation window relative to that primary episode
  • Evaluates the occurrence of multiple secondary events

Events can be defined as an interval (i.e., an episode) or as a single point in time. The tool gives users the flexibility to specify the observation window to be before, during, or after the primary treatment episode. Secondary cohort events are only considered if they fall in a requester-defined observation window. 

Output metrics include: 

  • Number of primary treatment episodes with multiple events
  • Number of exposed patients with multiple events
  • Total duration of primary treatment episode

Continue reading about identifying multiple events on Sentinel's Git Repository.

What this program does: 

  • Characterizes the overlap between primary and secondary treatment episodes during the observation window

The observation window is user-defined relative to the first primary treatment episode, during which the program evaluates occurrence of secondary episodes. Users have the flexibility to specify the observation window to be before, during or after the primary treatment episode. Secondary episodes are only considered if they fall in a requester-defined observation window. 

Output metrics include: 

  • Number of overlap episodes
  • Number of patients with overlap episodes
  • Total duration of primary treatment episode
  • Number of overlap days
  • Number of primary episodes with at least one secondary episode
  • Number of users with at least one secondary episode​​​​​​

​​​​​Continue reading about identifying and characterizing treatment overlap on Sentinel's Git Repository.

What this program does:

  • Identifies live births
  • Computes pregnancy episodes based on those live birth events
  • Assesses the use of specific medical products both during pregnancy episodes and in a comparator group of women likely to not have delivered a live birth during the same time frame 

Output metrics include:

  • Number of pregnancy episodes 
  • Number of pregnancy episodes with medical product use
  • Existence of a pre-term or post-term pregnancy code

This program reports medical product use for both pregnancy episodes and comparator episodes according to:

  • Trimester of use
  • Gestational week
  • Maternal age
  • Calendar year of delivery

Continue reading about pregnancy episodes on Sentinel's Git Repository.

What this program does: 

  • Identifies exposures of interest
  • Creates episodes of medical product exposure
  • Characterizes patient use and dispensing patterns

Output metrics include:

  • Number of patients, episodes, dispensings, and days supply by sex and age group (for the first patient episode or all observed episodes during the query period)
  • Number of episodes by episode number, episode length, sex, age group, and reason(s) for censoring
  • Number of episode gaps by gap number, gap length, sex and age group 
  • Race, ethnicity, geographic region (available by request)

Continue reading about medical product utilization on Sentinel's Git Repository.

What this program does: 

  • Identifies product groups by user-defined lists of product codes, e.g., NDCs, grouped together to represent distinct manufacturer-level products 
  • Characterizes patterns of product utilization
  • Evaluates patient-level switching behavior between manufacturer-level product groups

Output metrics include:

  • Counts of users and dispensings
  • Days supplied per dispensing
  • Episode duration
  • Time to uptake
  • Counts of switch pattern episodes
  • Switch pattern episode duration

Continue reading about manufacturer-level product utilization and switching patterns on Sentinel's Git Repository.

Want more details on the functional and technical documentation of each Level 1 cohort identification strategy? Visit Sentinel's Git Repository.    

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